Prediction of Students Drop Out With Naïve Bayes
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Abstract
The increasing number of non-active students at Mulia University PSDKU Samarinda makes the risk of student drop out increasing. The purpose of this study is to provide predictive results for students dropping out at Mulia University PSDKU Samarinda with the Naïve Bayes algorithm, so that from an early age it can know the characteristics of students dropping out and can reduce the number of students dropping out. 2 data sets are used, namely 1) Student graduation data which has 15 attributes and 290 records including name, study program, GPA, year of entry, year of exit, graduation, year, month, day, total in months, academic year, and predicate graduation. 2) Pulled data Feeder which contains 4 (fours) attributes and 407 records (only counting the retrieved data) spread over several files and merging or integrating the data. The data used for the undergraduate level is student data who entered in 2012, 2013, and 2014 which amounted to 280 records. At the D3 level used, data from students who entered in 2012, 2013, 2014, 2015, 2016, and 2017 amounted to 127 records. The result is that D3 level dropout students have an accuracy rate of 89.47% with 100% precision for drop out students. The S1 level obtained an accuracy of 96.43% with a student dropout precision of 88.46%.
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